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Record W2114765208

A Comparative Analysis between Small and Medium Scale Manufacturing Company through Total Quality Management Techniques

2013· article· en· W2114765208 on OpenAlexvenueno aff
Mohammad Israr, Anshul Gangele

Bibliographic record

VenueMechanical Engineering Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisTotal quality managementQuality (philosophy)BusinessBenchmark (surveying)Process (computing)Process managementQuality managementStrengths and weaknessesScale (ratio)BenchmarkingSet (abstract data type)Competitive advantageMarketingOperations managementKnowledge managementComputer scienceEngineeringService (business)
DOInot available

Abstract

fetched live from OpenAlex

Objective is strategic goal, set by the organization through its strategic planning process for attainment of vision in larger frame of the future. Today, find much literature which abounds with case studies of successful companies, descriptions of quality concepts, and quality improvement approaches. However, most of this literature is rather weak in providing benchmark data or a framework that managers can use to evaluate and modify their own quality improvement efforts. This framework is important because it can be used to assess the status of quality practices in organizations and guide managers in their quality improvement initiatives, without being dependent on outside consultants. It is also useful in providing initial benchmark data about the strengths and weaknesses that exist within the firm in the process of pursuing a quality improvement program. Smaller firms are likely to face more risk in attempting technological or organizational change. Because large and medium companies can assess greater resources, they are more likely to survive a stumble when adopting a new technology or trying a new management concept such as TQM. Consequently, this causes smaller firms to place great importance on doing it right the first time. They may not get a second chance. It is also helped small and medium size enterprises--would be able to design and implement their own less expensive and easy to follow TQM criteria, which ultimately will produce competent internal results for their company. In this respect, primary objective of TQM can be simply states as to ensure 'Performance Superiority' of the company over competitors by delivering total customer satisfaction. Secondary objectives of TQM are- 1. Continuous improvement of the organizational processes and output, which must be equal or superior to the competition 2. Continual and relentless cost reduction and value addition to products and services 3. Continuous and relentless thrust for improvement of manufacturing processes, product and services 4. Creation of an organizational work culture where by everyone is involved in the process of customer satisfaction and value creation for its customers 5. Making the organization market and customer focused 6. Guiding the organization its values, vision, mission and goal set through 'strategic planning' process 7. Changing the organization from 'function' focused to 'customer' focused, where customer's priorities come first in all activities 8. Making the organization flexible and learning oriented to cope with change; change in market place,business-environment, opportunities for improvements as well as organizational culture 9. Creating an organization where people are at the core of every activity, and are encouraged and empowered to work in teams 10. Promoting a transparent leadership process to lead the organization to excellence in its chosen field of business

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.093
GPT teacher head0.344
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2013
Admission routes1
Has abstractyes

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